Nutrient Composition of Cereals-based Complementary Flour and its Nutritional Adequacy in Infants Nutrition
Bibliographic record
Abstract
Evaluation of the quality of complementary flour and its nutritional adequacy in relation to infant’s nutritional requirements was done to four different types of complementary flour (composite cereals with groundnuts, maize, millet and sorghum). The collected samples were analysed for determination of macronutrient, micronutrients and phytate content. The mean protein content in all types of complementary flour was 7.30 ± 2.52 g/100 g. A significant difference in protein content between composite cereals (p ≤ 0.001) and other types of cereals-based foods was observed. Fat and iron content were found to be inadequate in all types of complementary flours. Phytate content ranged from 59.47 - 1176.8 mg/100 g, the highest content was observed in sorghum. On the other hand, the mean iron and zinc content in all types of complementary flour was 5.25 ± 1.35 mg/100 g and 2.99 ± 1.36 mg/100 g respectively. The molar ratios of phytate:iron and phytate:zinc were at a level that would tend to inhibit bioavailability of iron and zinc in 97% and 45% of analysed samples respectively. An association between anaemia and phytate:iron molar ratio (AOR = 4.2, 95%, 1.2 - 6.9) was observed. The quality and adequacy of nutrients in cereal-based complementary flour has shown to be inadequate and likely to be compromised by the presence of phytate.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".